Monday, February 13, 2012

Why your innovation ecosystem MUST make use of Semantics


Here’s a scenario worth thinking about. Let’s say you deploy your internal social network. And it’s a huge success. Everyone is posting information. You ask for ideas and you get them. But now you’re faced with a dilemma….How do you pull all that information together? How do you surface the best ideas?

The problem is too much information. Just the opposite of where you started. You have too many ideas to manually sift through all of them. Many of them are similar but you wonder how you’re going to match those similar ideas together.

You can manually tag all of the ideas people post with “key words”. You can ask those who post their ideas to tag them with key words. If you have a similarity algorithm running (You DO have a similarity engine running don’t you?) your internal social network can point out relevant (similar) ideas.

But if this internal social network is a success, then everyone who’s using it is posting comments. Are you tagging comments? Probably not. And are all the inventors properly tagging every idea they post? Probably not. And what about all the other objects in your innovation social network…are they getting tagged? Surely there’s value in the non-idea postings (otherwise you wouldn’t be encouraging your user community to post them). How do you tie together those objects (discussions, links, events, people’s profiles)?

Well there’s good news. The answer to “how to best mine the tons of data in your internal social network” is SEMANTICS. Yes, that’s right. Your internal social network requires a semantics engine. And what does this semantics engine do? It reads the title and description of users’ postings. It reads people’s ideas and discussions and their comments. It identifies relevant information and makes it easy for you to find similar postings.

I’ll talk about what you do with similar information in a few paragraphs from now but for the moment….Here’s the first reason you need a semantics engine in your ideation process: There is so much information submitted in the collaborative tool, that there are undiscovered relationships out there you absolutely need to identify to optimally surface the best idea.

Semantics is not a new set of technologies, but its use is new when applied to the ideation process. As Wikipedia states, “Semantics is the study of meaning. It focuses on the relation between signifiers, such as words, phrases, signs and symbols, and what they stand for, their denotata.” Why is it important? If you accept the fact the ideation process is best realized in a collaborative environment, if you accept the Wisdom of the Crowd is better than a lone inventor holed up in a tower, then you have to find ways to connect an enormous amount of information.
Different people from different cultures (your Russian, Polish, French, Italian, Japanese, American teammate), from different disciplines (the Engineers, Marketers, Financial people, Sales types you work with) all look at a problem differently and describe things differently. It’s not just the words they use, but how they use them that signify relationships between different concepts.
The range of words and concepts we bring to a discussion based on our background bears on our inner lives, and is reflected when we give our opinions. Every conception of the mind must set priorities, putting some experiences at the center and others at the periphery. A semantics engine can help you identify similar information enabling you to do something about it.

In a real world innovation system environment, rarely does the best idea work its way toward “project” status alone. It usually is a conglomeration of multiple ideas. That’s why you use a collaborative tool, an internal social network, to begin with. You want lots of input. Your ideation system needs to be able to do two things to enable this:

1. You need to periodically merge ideas into one single concept. So for instance, an engineer suggests a new widget. Another engineer suggests a slightly different widget that serves almost the same purpose. A third person suggests a way to tweak the first widget making it marketable to additional marketplaces. A marketing person posts an idea about how to serve a certain market that can be satisfied by the widget in question. A good internal social network can identify the similarities amongst all those postings and let you merge them into one overall concept: A widget in two flavors, with variability to satisfy more than one market.

2. You need to periodically “cluster” multiple ideas. This is different than merging. In this case the ideas need to remain separate yet have a mutual affinity logically requiring them to travel through the idea promotion process together. Widget X gets suggested that does one thing. Widget Y gets suggested that does another thing. But the manufacturing process is identical. Clustering lets these ideas get considered both individually and as a group.

Semantics engines learn, by the way. When you start using it, it already “knows” that certain words should be ignored (I, is, are, we, they, it). As it gets used, the semantics engine “learns” the value of certain words in your company’s vocabulary. If “widget” is acknowledged by the engine it will keep an eye out for “widget”. In fact if “widget” is used sometimes with “curved” and other times with “straight”, the semantics engine will learn the distinction and point out the relevant occurrences.

It is really wonderful companies are embracing internal social networks. These companies get Organizational Engagement. But like Facebook, there is so much voluntarily posted information that it becomes a challenge to sift through it all. Finding relevant information through a semantics engine, which has over time learned your company’s interests, enables the company to discover, build and shape valuable ideas.

If your company also has idea management as part of this internal social network you can assemble similar ideas into a cohesive bigger, better, actionable idea. You can avoid manually sifting through hundreds of ideas enabled with similarity search and automatic idea promotion based on both voting and participation in the idea’s shaping.

Kudos to your company if you’re embracing an internal social network. A condolence to your company if there’s no idea management component because that means some poor shmo is manually sifting through hundreds of ideas to find the best ones. Or worse, the ideas go unacknowledged and inventors stop submitting their future thoughts. Semantics is the future pathway to mine tremendous amounts of voluntarily submitted information in a collaborative system.

Your collaborative system provides tons of good information. It is the ability to find relevancy amongst all this data that pays for the system.

ABOUT THE AUTHOR:

Ron Shulkin is Vice President for CogniStreamer®, an innovation ecosystem software system. You can learn more about CogniStreamer here http://bit.ly/ac3x60

Ron manages The Idea Management Group on LinkedIn (Join Here) http://bit.ly/dvsYWD . You can read more of his work by searching on his last name, Shulkin, here at the FEI web site with almost weekly guest blog entries.
CogniStreamer® is an idea management software tool. It is an open innovation and collaboration platform where internal colleagues and external partner companies or knowledge centers join forces to create, develop and assess innovative ideas within strategically selected areas. The CogniStreamer® portal is an ideal collaborative platform that invites users to actively build a strong innovation portfolio. In addition it provides a powerful resource for internal and external knowledge sharing. CogniStreamer clients consider our innovation ecosystem a Knowledge Management System, an Idea Management System and, in fact, a social network for innovation. The CogniStreamer® framework is used by industry leaders such as Atlas Copco, Bekaert, Case New Holland, Cytec, Imec, Phillip Morris, Picanol ThyssenKrupp, Vesuvius and many more. CogniStreamer® represents the best use of adaptive collaborative technology such to harness human skill, ingenuity and intelligence.

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